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Local explanations of learning-to-rank (LTR) models are thought to extractthe most important features that contribute to the ranking predicted by the LTRmodel for a single data point. Evaluating the accuracy of such explanations ischallenging since the ground truth feature importance scores are not availablefor most modern LTR models. In this work, we propose a systematic evaluationtechnique for explanations of LTR models. Instead of using black-box models,such as neural networks, we propose to focus on tree-based LTR models, fromwhich we can extract the ground truth feature importance scores using decisionpaths. Once extracted, we can directly compare the ground truth featureimportance scores to the feature importance scores generated with explanationtechniques. We compare two recently proposed explanation techniques for LTRmodels when using decision trees and gradient boosting models on the MQ2008dataset. We show that the explanation accuracy in these techniques can largelyvary depending on the explained model and even which data point is explained.

Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths